A Bayesian Model of Rule Induction in Raven's Progressive Matrices

Daniel R. Little, The University of Melbourne

Stephan Lewandowsky, The University of Western Australia

Thomas L. Griffiths, University of California Berkeley

Abstract

Raven's Progressive Matrices is one of the most prevalent assays
of fluid intelligence; however, most theoretical accounts of Raven's focus on
producing models which can generate the correct answer but do not fit human
performance data. We provide a computational-level theory which interprets rule
induction in Raven's as Bayesian inference. The model computes the posterior
probability of each rule in the set of possible rule hypotheses based on whether
those rules could have generated the features of the objects in the matrix and
the prior probability of each rule. Based on fits to both correct and incorrect
response options across both the Standard and Advanced Progressive Matrices, we
propose several novel mechanisms that may drive responding to Raven's items.